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Abstract:

Described is a method of classifying a scene for each person in a video,
the method including: detecting a face within input video frames;
detecting a shot change of the input video frames; extracting a person
representation frame in the shot; performing a person clustering in the
extracted person representation frame based on time information;
detecting a scene change by separating a person portion from a background
based on face extraction information, and comparing the person portion
and the background; and merging similar clusters from the extracted
person representation frame and performing a scene clustering for each
person.

Claims:

1. A method of classifying a scene for each person in a video, the method
comprising: extracting a person representation frame in a shot; comparing
a first person representation frame and a second person representation
frame; performing a person clustering by extending a time window when the
first person representation frame is similar to the second person
representation frame; merging similar clusters using a person cluster
extracted from a representation frame and performing a scene clustering
for each person based on a scene change, wherein the scene change is
determined using a person portion and a background portion.

2. The method of claim 1, wherein the performing of the person clustering
further comprises: receiving cluster information, the first person
representation frame, and the second person representation frame to be
compared; including the second person representation frame which has been
currently compared in the current cluster information when the first
person representation frame is similar to the second person
representation frame; and setting a subsequent person representation
frame as third person representation frame to be compared on the time
window.

3. The method of claim 2, further comprising: moving to a subsequent
cluster when the first person representation frame and the second person
representation frame are at the end of the time window; or setting the
subsequent person representation frame in the time window as the other
person representation frame to be compared on the time window, when the
first person representation frame and the second person representation
frame to be compared are not at the end of the time window.

4. The method of claim 1, wherein the performing of the scene clustering
comprises: receiving time information-based clusters; selecting two
clusters having a minimum difference value; comparing the minimum
difference value and a threshold value; and merging the two clusters when
the minimum difference value is less than the threshold value.

5. A non-transitory computer-readable recording medium storing a program
for implementing a method of classifying a scene for each person in a
video, the method comprising: extracting a person representation frame in
a shot; comparing a first person representation frame and a second person
representation frame; performing a person clustering by extending a time
window when the first person representation frame is similar to the
second person representation frame; merging similar clusters using a
person cluster extracted from a representation frame and performing a
scene clustering for each person based on a scene change, wherein the
scene change is determined using a person portion and a background
portion.

6. A system for classifying a scene for each person in a video, the
system comprising: a person representation frame extracting unit to
extract a person representation frame in a shot; a person clustering unit
to compare a first person representation frame and a second person
representation frame and to perform a person clustering by extending a
time window when the first person representation frame is similar to the
second person representation frame; a scene clustering unit to merge
similar clusters using a person cluster extracted from a representation
frame and to perform a scene clustering for each person based on a scene
change, wherein the scene change is determined using a person portion and
a background portion.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation of application Ser. No.
11/882,733 filed on Aug. 3, 2007, which claims the priority of Korean
Patent Application No. 10-2007-0000957, filed on Jan. 4, 2007, in the
Korean Intellectual Property Office, the disclosure of which is
incorporated herein by reference.

BACKGROUND

[0002] 1. Field

[0003] The present invention relates to a method and system for
classifying a scene for each person in a video, and more particularly, to
a method and system for classifying a scene for each person in a video
based on person information and background information in video data.

[0004] 2. Description of the Related Art

[0005] Generally, a scene is a unit between when video contents are
changed. In a conventional art, scenes are classified by using low level
information such as color information or edge information.

[0006] Specifically, shots are clustered using low level information such
as color information extracted in all frames, and a scene segmentation is
detected in a conventional automatic scene segmentation algorithm.
However, when a person in a video moves or a camera moves, low level
information changes. Accordingly, a degree of accuracy decreases.

[0007] Also, persons in a video are clustered using face information, and
thus the persons are classified in a conventional person classification
method. However, face information changes depending on poses, lighting,
and the like, which causes a low accuracy.

[0008] Accordingly, a method and system for classifying a scene for each
person in a video is required.

SUMMARY

[0009] An aspect of the present invention provides a method and system for
classifying a scene for each person in a video which may provide a story
overview for each person by classifying a person by a scene unit by using
temporal information in video data.

[0010] An aspect of the present invention also provides a method and
system for classifying a scene for each person in a video which may
improve an accuracy of a scene segmentation detection by separating a
person portion and a background in video data and using information about
the person portion and the background together.

[0011] According to an aspect of the present invention, there is provided
a method of classifying a scene for each person in a video, the method
including: detecting a face within input video frames; detecting a shot
change of the input video frames; extracting a person representation
frame in the shot; performing a person clustering in the extracted person
representation frame based on time information; detecting a scene change
by separating a person portion from a background based on face extraction
information, and comparing the person portion and the background; and
merging similar clusters from the extracted person representation frame
and performing a scene clustering for each person.

[0012] According to another aspect of the present invention, there is
provided a system for classifying a scene for each person in a video, the
system including: a face detection unit detecting a face within input
video frames; a shot change detection unit detecting a shot change of the
input video frames; a person representation frame extraction unit
extracting a person representation frame in the shot; a person clustering
unit performing a person clustering in the extracted person
representation frame based on time information; a scene change detection
unit detecting a scene change by separating a person portion from a
background based on face extraction information and comparing the person
portion and the background; and a scene clustering unit merging similar
clusters from the extracted person representation frame and performing a
scene clustering for each person.

[0013] Additional aspects and/or advantages of the invention will be set
forth in part in the description which follows and, in part, will be
apparent from the description, or may be learned by practice of the
invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office upon
request and payment of the necessary fee.

[0015] These and/or other aspects, features, and advantages of the
invention will become apparent and more readily appreciated from the
following description of exemplary embodiments, taken in conjunction with
the accompanying drawings of which:

[0016] FIG. 1 is a block diagram illustrating a configuration of a system
for classifying a scene for each person in a video according to an
embodiment of the present invention;

[0017] FIG. 2 is a diagram illustrating an example of clothes information
and face information detected in a same time window according to an
embodiment of the present invention;

[0018] FIG. 3 is a diagram illustrating an example of performing a
clustering for each person according to an embodiment of the present
invention;

[0019] FIG. 4 is a flowchart illustrating a method of classifying a scene
for each person in a video according to another embodiment of the present
invention;

[0020] FIG. 5 is a flowchart illustrating an operation of a time
information-based person clustering illustrated in FIG. 4 according to
another embodiment of the present invention;

[0021] FIG. 6 is a flowchart illustrating an operation of a scene change
detection illustrated in FIG. 4 according to another embodiment of the
present invention; and

[0022] FIG. 7 is a flowchart illustrating an operation of a scene
clustering for each person according to another embodiment of the present
invention.

DETAILED DESCRIPTION

[0023] Reference will now be made in detail to embodiments of the present
invention, examples of which are illustrated in the accompanying
drawings, wherein like reference numerals refer to the like elements
throughout. The embodiments are described below in order to explain the
present invention by referring to the figures.

[0024] FIG. 1 is a block diagram illustrating a configuration of a system
for classifying a scene for each person in a video according to an
embodiment of the present invention.

[0025] Referring to FIG. 1, the system for classifying a scene for each
person in a video 100 includes a face detection unit 110, a shot change
detection unit 120, a person representation frame extraction unit 130, a
person clustering unit 140, a scene change detection unit 150, and a
scene clustering unit 160.

[0027] The shot change detection unit 120 detects a shot change within the
input video frames. Specifically, the shot change detection unit 120
detects the shot change of the input video frames to segment the input
video frames into a shot which is a basic unit of the video.

[0028] The person representation frame extraction unit 130 extracts a
person representation frame in the shot. Using all person frames for a
person clustering is inefficient. Accordingly, the person representation
frame extraction unit 130 extracts a frame which is closest to a center
frame having a greatest similarity in each cluster as the person
representation frame, after performing a clustering of frames including a
face in the shot. Specifically, the person representation frame
extraction unit 130 extracts the frame one by one in all clusters and may
set the frame as the person representation frame in the shot, since at
least one person may be included in the shot.

[0029] The person clustering unit 140 performs the person clustering in
the extracted person representation frame based on time information. When
simply performing a clustering based on all person representation frames,
an algorithm for various poses or lightings may not be strict.
Accordingly, the person clustering unit 140 performs the person
clustering by using the time information to start clustering based on
various forms of each person. Specifically, as illustrated in FIG. 2, a
single person generally wears same clothes within a similar time period
in same video data, and such clothes information has a clearer difference
than face information. Accordingly, the person clustering unit 140
obtains various forms of the single person by using the clothes
information.

[0030] FIG. 2 is a diagram illustrating an example of clothes information
and face information detected in a same time window according to an
embodiment of the present invention.

[0031] A location and size of a face 211, 221, 231, 241, and 251,
automatically detected in a person representation frame 210, 220, 230,
240, and 250 in a shot, and a location and size of clothes 212, 222, 232,
242, and 252, extracted in the person representation frame 210, 220, 230,
240, and 250, are illustrated in FIG. 2. The size of clothes is
determined in proportion to a size of a key person in the person
representation frame 210, 220, 230, 240, and 250 in the shot.

[0032] The person clustering unit 140 extracts clothes information from
current cluster information, a current person representation frame, and a
comparison person representation frame, i.e. a person representation
frame to be compared. The person clustering unit 140 compares the current
person representation frame and the comparison person representation
frame, and determines whether the current person representation frame is
similar to the comparison person representation frame as a result of the
comparing. The person clustering unit 140 extends a time window when the
current person representation frame is similar to the comparison person
representation frame, and includes the person representation frame which
has been currently compared in the current cluster information. The
person clustering unit 140 sets a subsequent person representation frame
as another comparison person representation frame on the time window.
Also, the person clustering unit 140 determines whether the current
person representation frame and the comparison person representation
frame are at an end of the time window, when the current person
representation frame is different from the comparison person
representation frame. The person clustering unit 140 sets the subsequent
person representation frame in the time window as the other comparison
person representation frame, when the current person representation frame
and the comparison person representation frame are not at the end of the
time window.

[0033] A scene change detection unit 150 detects a scene change by
separating a person portion from a background based on face extraction
information and comparing the person portion and the background.
Specifically, the scene change detection unit 150 may approximately
extract a person by using the face extraction information, and thus may
detect the scene change by the separating and the comparing after the
person is approximately extracted.

[0034] The scene change detection unit 150 receives current scene
information, a current shot representation frame, and a comparison shot
representation frame, and extracts background information from the
current shot representation frame and the comparison shot representation
frame. The scene change detection unit 150 compares the current shot
representation frame and the comparison shot representation frame, and
determines whether the current shot representation frame is similar to
the comparison shot representation frame. The scene change detection unit
150 extends the time window when the current shot representation frame is
similar to the comparison shot representation frame, and marks that the
comparing of the current shot representation frame is completed. The
scene change detection unit 150 assigns the comparison shot
representation frame to the current shot representation frame, and
assigns a subsequent shot representation frame in the time window to the
comparison shot representation frame. The scene change detection unit 150
marks that the comparing of the current shot representation frame is
completed, when the current shot representation frame is different from
the comparison shot representation frame, and determines whether
comparing all frames in the time window is completed. The scene change
detection unit 150 assigns a subsequent shot representation frame where
the comparing is incomplete to the current shot representation frame, and
assigns the subsequent shot representation frame to the comparison shot
representation frame, when the comparing is incomplete.

[0035] A scene clustering unit 160 merges similar clusters from the
extracted person representation frame and performs a scene clustering for
each person. Specifically, the scene clustering unit 160 may perform the
scene clustering for each person by comparing the person representation
frame in the shot and merging the similar clusters according to the
comparison, as illustrated in FIG. 3.

[0036] The scene clustering unit 160 receives time information-based
clusters, and selects two clusters having a minimum difference value. The
scene clustering unit 160 compares the minimum difference value and a
threshold value, and merges the two clusters when the minimum difference
value is less than the threshold value. The scene clustering detection
unit 160 connects scenes including a person frame in a same cluster, when
the minimum difference value is equal to or greater than the threshold
value. A scene clustering method for each person is described in greater
detail with reference to FIG. 3.

[0037] FIG. 3 is a diagram illustrating an example of performing a
clustering for each person according to an embodiment of the present
invention.

[0038] In operation S1, a scene clustering unit 160 compares a first
person representation frame 310 and a second person representation frame
320, and performs a first merge of similar clusters based on a result of
the comparison. In operation S2, the scene clustering unit 160 compares a
fifth person representation frame 350 and a sixth person representation
frame 360, and performs a second merge of similar clusters based on a
result of the comparison. In operation S3, the scene clustering unit 160
compares a third person representation frame 330 and a seventh person
representation frame 370, and performs a third merge of similar clusters
based on a result of the comparison. In operation S4, the scene
clustering unit 160 compares the first merge and the second merge, and
performs a fourth merge of similar clusters based on a result of the
comparison.

[0039] FIG. 4 is a flowchart illustrating a method of classifying a scene
for each person in a video according to another embodiment of the present
invention.

[0040] Referring to FIG. 4, in operation S410, a system for classifying a
scene for each person in a video detects a face within input video
frames. Specifically, the system for classifying a scene for each person
in a video analyzes the input video frames via a face detector and
thereby may detect the face within the input video frames.

[0041] In operation S420, the system for classifying a scene for each
person in a video detects a shot change within the input video frames.
Specifically, the system for classifying a scene for each person in a
video detects the shot change within the input video frames to segment
the input video frames into a shot which is a basic unit of the video.

[0042] In operation S430, the system for classifying a scene for each
person in a video extracts a person representation frame in the shot.
Since using all person frames for a person clustering is inefficient, the
system for classifying a scene for each person in a video extracts a
frame which is closest to a center in each cluster as the person
representation frame, after performing a clustering of frames including a
face in the shot. Specifically, the system for classifying a scene for
each person in a video extracts the frame one by one in all frames and
may set the frame as the person representation frame in the shot, since
at least one person may be included in the shot.

[0043] In operation S440, the system for classifying a scene for each
person in a video performs the person clustering in the extracted person
representation frame based on time information. When simply clustering
based on all person representation frames, an algorithm for various poses
or lightings may not be strict. Accordingly, the system for classifying a
scene for each person in a video performs the person clustering by using
the time information to start clustering based on various forms of each
person. Specifically, a single person generally wears the same clothes
within a similar time period in the same video data, and such clothes
information has a clearer difference than face information. Accordingly,
the system for classifying a scene for each person in a video obtains
various forms of the single person by using the clothes information. An
operation of a time information-based person clustering is described in
greater detail with reference to FIG. 5.

[0044] FIG. 5 is a flowchart illustrating an operation of a time
information-based person clustering illustrated in FIG. 4 according to
another embodiment of the present invention.

[0045] Referring to FIG. 5, in operation S501, the system for classifying
a scene for each person in a video receives current cluster information,
a current person representation frame, and a comparison person
representation frame. The comparison person representation frame is a
person representation frame to be compared.

[0046] In operation S502, the system for classifying a scene for each
person in a video extracts clothes information of each of the current
person representation frame and the comparison person representation
frame. Specifically, the system for classifying a scene for each person
in a video may extract the clothes information by referring to the
location and size of the face from the face information as illustrated in
FIG. 2 to reduce a time to extract clothes information.

[0047] In operation S503, the system for classifying a scene for each
person in a video compares the current person representation frame and
the comparison person representation frame. Specifically, the system for
classifying a scene for each person in a video adds a comparison value of
color information corresponding to the clothes information and a weight
of a comparison value corresponding to the face information, when
comparing.

[0048] In operation S504, the system for classifying a scene for each
person in a video determines whether the current person representation
frame is similar to the comparison person representation frame, as a
result of the comparing.

[0049] In Operation S505, when the current person representation frame is
similar to the comparison person representation frame, the system for
classifying a scene for each person in a video extends a time window
Tfw. Specifically, when the current person representation frame is
similar to the comparison person representation frame, the system for
classifying a scene for each person in a video resets the time window
Tfw from a present point in time, since a same person exists up to
the present point in time.

[0050] In operation S506, the system for classifying a scene for each
person in a video includes the comparison person representation frame
which has been currently compared in the current cluster information.
Specifically, the system for classifying a scene for each person in a
video includes the comparison person representation frame, which has been
compared with the current person representation frame, in the current
cluster information.

[0051] In operation S507, the system for classifying a scene for each
person in a video sets a subsequent person representation frame in the
time window Tfw as other comparison person representation frame, and
performs operation S502. Specifically, the system for classifying a scene
for each person in a video continues to compare using the subsequent
person representation frame in the time window Tfw.

[0052] In operation S508, when the current person representation frame is
different from the comparison person representation frame, the system for
classifying a scene for each person in a video determines whether the
current person representation frame and the comparison person
representation frame are at an end of the time window Tfw.
Specifically, when the current person representation frame is different
from the comparison person representation frame, the system for
classifying a scene for each person in a video determines whether the all
frames in the time window Tfw are compared by using a result of the
determining whether the current person representation frame and the
comparison person representation frame are at the end of the time window
Tfw.

[0053] In operation S509, when the current person representation frame and
the comparison person representation frame are at the end of the time
window Tfw, the system for classifying a scene for each person in a
video moves to a subsequent cluster and performs a time information-based
person clustering for the subsequent cluster, since all person
representation frames corresponding to a current cluster are extracted.

[0054] In operation S510, when the current person representation frame and
the comparison person representation frame are not at the end of the time
window Tfw, the system for classifying a scene for each person in a
video sets the subsequent person representation frame as the comparison
person representation frame, and performs operation S502, since the all
person representation frames corresponding to the current cluster are not
detected.

[0055] In operation S450, the system for classifying a scene for each
person in a video detects a scene change by separating a person portion
from a background based on face extraction information and comparing the
person portion and the background. Specifically, the system for
classifying a scene for each person in a video may approximately extract
a person by using the face extraction information, and thus may detect
the scene change by the separating and the comparing after the person is
approximately extracted. A scene change detection operation is described
in greater detail with reference to FIG. 6.

[0056] FIG. 6 is a flowchart illustrating an operation of a scene change
detection illustrated in FIG. 4 according to another embodiment of the
present invention.

[0057] Referring to FIG. 6, in operation S601, the system for classifying
a scene for each person in a video receives current scene information, a
current shot representation frame Pf, and a comparison shot
representation frame Cf.

[0058] In operation S602, the system for classifying a scene for each
person in a video extracts background information of the current shot
representation frame Pf and the comparison shot representation frame
Cf. The background information is information about a pixel of
another location excluding a face location and a clothes location.

[0059] In operation S603, the system for classifying a scene for each
person in a video compares the current shot representation frame Pf
and the comparison shot representation frame Cf. Specifically, the
system for classifying a scene for each person in a video adds the
comparison value of the color information corresponding to the clothes
information and the weight of the comparison value corresponding to the
face information, when comparing. Also, when comparing the background
information, a normalized color histogram, and a hue, saturation, value
(HSV) are used.

[0060] In operation S604, the system for classifying a scene for each
person in a video determines whether the current shot representation
frame Pf is similar to the comparison shot representation frame
Cf, as a result of the comparing.

[0061] In operation S605, when the current shot representation frame
Pf is similar to the comparison shot representation frame Cf,
the system for classifying a scene for each person in a video extends a
time window Tsw. Specifically, the system for classifying a scene
for each person in a video resets the time window Tsw to extend a
scene again, since a same scene is continued up to a point in time when
the current shot representation frame Pf is similar to the
comparison shot representation frame Cf.

[0062] In operation S606, the system for classifying a scene for each
person in a video marks that the comparing of the current shot
representation frame Pf is completed, and sets the comparison shot
representation frame Cf as the current shot representation frame
Pf.

[0063] In operation S607, the system for classifying a scene for each
person in a video sets a subsequent shot representation frame in the time
window Tsw as a comparison shot representation frame (*Cf?),
and performs operation S602. Specifically, the system for classifying a
scene for each person in a video continues to compare using the
subsequent shot representation frame in the time window Tsw.

[0064] In operation S608, when the current shot representation frame
Pf is different from the comparison shot representation frame
Cf, the system for classifying a scene for each person in a video
marks that the comparing of the current shot representation frame Pf
is completed.

[0065] In operation S609, the system for classifying a scene for each
person in a video determines whether comparing all frames in the time
window Tsw is completed.

[0066] In operation S610, when the comparing all frames in the time window
Tsw is completed, the system for classifying a scene for each person
in a video determines a shot, which is examined last and determined to be
a similar shot, as a last shot of a current scene, since all shots
corresponding to the current scene are detected. Also, the system for
classifying a scene for each person in a video performs a detection
operation of a subsequent scene.

[0067] In operation S611, when the comparing is incomplete, the system for
classifying a scene for each person in a video sets a subsequent shot
representation frame where the comparing is incomplete as the current
shot representation frame Pf, and sets the subsequent shot
representation frame as the comparison shot representation frame Cf.
Also, the system for classifying a scene for each person in a video
performs operation S602.

[0068] In operation S460, the system for classifying a scene for each
person in a video merges similar clusters from the extracted person
representation frame and performs the scene clustering for each person.
Specifically, the system for classifying a scene for each person in a
video may perform the scene clustering by comparing and merging as
illustrated in FIG. 3. An operation of a scene clustering for each person
is described in greater detail with reference to FIG. 7.

[0069] FIG. 7 is a flowchart illustrating an operation of a scene
clustering for each person according to another embodiment of the present
invention.

[0070] Referring to FIG. 7, in operation S701, the system for classifying
a scene for each person in a video receives time information-based
clusters.

[0071] In operation S702, the system for classifying a scene for each
person in a video selects two clusters having a minimum difference value
from difference values from among all clusters. Specifically, the
difference values of all clusters may be compared using an average value
of each cluster. Also, the minimum difference value may be used after
comparing all objects of a corresponding cluster and all objects of a
comparison cluster.

[0072] In operation S703, the system for classifying a scene for each
person in a video compares the minimum difference value and a threshold
value and determines whether the minimum difference value is less than
the threshold value.

[0073] In operation S704, when the minimum difference value is less than
the threshold value, the system for classifying a scene for each person
in a video merges the two clusters, as illustrated in FIG. 3, since the
two clusters include a similar person. Also, the system for classifying a
scene for each person in a video performs operation S702.

[0074] In operation S705, when the minimum difference value is equal to or
greater than the threshold value, the system for classifying a scene for
each person in a video connects scenes including a person frame in a same
cluster. Specifically, the system for classifying a scene for each person
in a video determines that all clustering are completed when the minimum
difference value is equal to or greater than the threshold value. Also,
when connecting the scenes including a same person, the operation of a
scene clustering for each person is completed. Each scene may be included
in many clusters since various persons may exist in a single scene.

[0075] The method and system for classifying a scene for each person in a
video according to the above-described exemplary embodiments of the
present invention may be recorded in computer-readable media including
program instructions to implement various operations embodied by a
computer. The media may also include, alone or in combination with the
program instructions, data files, data structures, and the like. Examples
of computer-readable media include magnetic media such as hard disks,
floppy disks, and magnetic tape; optical media such as CD ROM disks and
DVD; magneto-optical media such as optical disks; and hardware devices
that are specially configured to store and perform program instructions,
such as read-only memory (ROM), random access memory (RAM), flash memory,
and the like. The media may also be a transmission medium such as optical
or metallic lines, wave guides, etc. including a carrier wave
transmitting signals specifying the program instructions, data
structures, etc. Examples of program instructions include both machine
code, such as produced by a compiler, and files containing higher level
code that may be executed by the computer using an interpreter. The
described hardware devices may be configured to act as one or more
software modules in order to perform the operations of the
above-described exemplary embodiments of the present invention.

[0076] A method and system for classifying a scene for each person in a
video according to the above-described embodiments of the present
invention may provide a story overview for each person by classifying a
person by a scene unit by using temporal information in video data.

[0077] Also, a method and system for classifying a scene for each person
in a video according to the above-described embodiments of the present
invention may improve an accuracy of a scene segmentation detection by
separating a person portion and a background in video data and using
information about the person portion and the background together.

[0078] Also, a method and system for classifying a scene for each person
in a video according to the above-described embodiments of the present
invention may replay for each person in video data, and thereby may
enable a user to selectively view a scene including a person that the
user likes.

[0079] Also, a method and system for classifying a scene for each person
in a video according to the above-described embodiments of the present
invention may classify a person by a scene unit, which is a story unit in
video data, and thereby may improve a scene classification accuracy and
enable a scene-based navigation.

[0080] Also, a method and system for classifying a scene for each person
in a video according to the above-described embodiments of the present
invention may perform a video data analysis more easily by improving a
scene classification accuracy in video data.

[0081] Although a few embodiments of the present invention have been shown
and described, the present invention is not limited to the described
embodiments. Instead, it would be appreciated by those skilled in the art
that changes may be made to these embodiments without departing from the
principles and spirit of the invention, the scope of which is defined by
the claims and their equivalents.